摘要翻译：
本文考虑了在一个可观测选择框架中，在可能的混杂因素数目很大的情况下，非参数估计随有限数目的离散和连续协变量变化的异构平均治疗效果的实际重要情况。我们提出了一个两步估计器，第一步由机器学习估计。我们证明了该估计具有良好的相合性、渐近正态性和速率双鲁棒性等统计性质。特别地，我们推导了非参数和机器学习步骤之间的耦合收敛条件。我们还表明，通过平均估计的异质效应来估计群体平均处理效应是半参数有效的。新的估计量是母亲在怀孕期间吸烟对由此产生的出生体重影响的一个实证例子。
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英文标题：
《Nonparametric estimation of causal heterogeneity under high-dimensional
  confounding》
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作者：
Michael Zimmert and Michael Lechner
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  This paper considers the practically important case of nonparametrically estimating heterogeneous average treatment effects that vary with a limited number of discrete and continuous covariates in a selection-on-observables framework where the number of possible confounders is very large. We propose a two-step estimator for which the first step is estimated by machine learning. We show that this estimator has desirable statistical properties like consistency, asymptotic normality and rate double robustness. In particular, we derive the coupled convergence conditions between the nonparametric and the machine learning steps. We also show that estimating population average treatment effects by averaging the estimated heterogeneous effects is semi-parametrically efficient. The new estimator is an empirical example of the effects of mothers' smoking during pregnancy on the resulting birth weight.
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PDF链接：
https://arxiv.org/pdf/1908.08779